import pandas as pd
import numpy as np
import os
import datetime
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn import tree
import xgboost as xgb
import pytz
import itertools
import visualize
import utils
import pydotplus
from sklearn import metrics
from sklearn import ensemble
from sklearn import linear_model
import pvlib
import cs_detection
import visualize_plotly as visualize
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
init_notebook_mode(connected=True)
from IPython.display import Image
%load_ext autoreload
%autoreload 2
np.set_printoptions(precision=4)
%matplotlib notebook
import warnings
warnings.filterwarnings('ignore')
Only making ground predictions using PVLib clearsky model and statistical model. NSRDB model won't be available to ground measurements.
nsrdb_srrl = cs_detection.ClearskyDetection.read_pickle('srrl_nsrdb_1.pkl.gz')
nsrdb_srrl.df.index = nsrdb_srrl.df.index.tz_convert('MST')
nsrdb_srrl.scale_by_irrad('Clearsky GHI pvlib')
nsrdb_srrl.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
nsrdb_abq = cs_detection.ClearskyDetection.read_pickle('abq_nsrdb_1.pkl.gz')
nsrdb_abq.df.index = nsrdb_abq.df.index.tz_convert('MST')
nsrdb_abq.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
nsrdb_abq.scale_by_irrad('Clearsky GHI pvlib')
nsrdb_ornl = cs_detection.ClearskyDetection.read_pickle('ornl_nsrdb_1.pkl.gz')
nsrdb_ornl.df.index = nsrdb_ornl.df.index.tz_convert('EST')
nsrdb_ornl.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
nsrdb_ornl.scale_by_irrad('Clearsky GHI pvlib')
Train model on all available NSRBD data
Scale model clearsky (PVLib)
nsrdb_srrl.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
nsrdb_abq.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
nsrdb_ornl.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
utils.calc_all_window_metrics(nsrdb_srrl.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
utils.calc_all_window_metrics(nsrdb_abq.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
utils.calc_all_window_metrics(nsrdb_ornl.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
feature_cols = [
'ghi_status',
'tfn',
'abs_ideal_ratio_diff',
'abs_ideal_ratio_diff mean',
'abs_ideal_ratio_diff std',
# 'abs_ideal_ratio_diff max',
# 'abs_ideal_ratio_diff min',
'GHI Clearsky GHI pvlib gradient ratio',
'GHI Clearsky GHI pvlib gradient ratio mean',
'GHI Clearsky GHI pvlib gradient ratio std',
# 'GHI Clearsky GHI pvlib gradient ratio min',
# 'GHI Clearsky GHI pvlib gradient ratio max',
'GHI Clearsky GHI pvlib gradient second ratio',
'GHI Clearsky GHI pvlib gradient second ratio mean',
'GHI Clearsky GHI pvlib gradient second ratio std',
# 'GHI Clearsky GHI pvlib gradient second ratio min',
# 'GHI Clearsky GHI pvlib gradient second ratio max',
'GHI Clearsky GHI pvlib line length ratio',
# 'GHI Clearsky GHI pvlib line length ratio gradient',
# 'GHI Clearsky GHI pvlib line length ratio gradient second'
]
target_cols = ['sky_status']
# best_params = {'max_depth': 5, 'n_estimators': 256}
# best_params = {'max_depth': 4, 'n_estimators': 256, 'class_weight': 'balanced'}
# best_params = {'max_depth': 4, 'n_estimators': 256, 'class_weight': None}
best_params = {'max_depth': 4, 'n_estimators': 256, 'class_weight': None}
clf = ensemble.RandomForestClassifier(**best_params, n_jobs=-1)
X = np.vstack((nsrdb_srrl.df[feature_cols].values,
nsrdb_abq.df[feature_cols].values,
nsrdb_ornl.df[feature_cols].values))
y = np.vstack((nsrdb_srrl.df[target_cols].values,
nsrdb_abq.df[target_cols].values,
nsrdb_ornl.df[target_cols].values))
print(int(X.shape[0] / 3) * 1000)
%%time
clf.fit(X, y.flatten())
ground = cs_detection.ClearskyDetection.read_pickle('srrl_ground_1.pkl.gz')
ground.df.index = ground.df.index.tz_convert('MST')
ground.trim_dates('10-01-2011', '10-08-2011')
ground.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status pvlib')
ground.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
ground.scale_by_irrad('Clearsky GHI pvlib')
test = ground
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 61, by_day=True, multiproc=True)
pred = pred.astype(bool)
vis = visualize.Visualizer()
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 0) & (pred)]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 1) & (~pred)]['GHI'], 'PVLib clear only')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 1) & (pred)]['GHI'], 'ML+PVLib clear only')
# vis.add_line_ser(test.df['abs_ideal_ratio_diff'] * 100)
vis.show()
probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')
ground = cs_detection.ClearskyDetection.read_pickle('srrl_ground_1.pkl.gz')
ground.df.index = ground.df.index.tz_convert('MST')
ground.trim_dates('10-01-2011', '10-08-2011')
ground.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
ground.scale_by_irrad('Clearsky GHI pvlib')
ground.df = ground.df.resample('30T').apply(lambda x: x[len(x) // 2])
test= ground
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 3, by_day=True, multiproc=True)
pred = pred.astype(bool)
vis = visualize.Visualizer()
srrl_tmp = cs_detection.ClearskyDetection(nsrdb_srrl.df)
srrl_tmp.intersection(ground.df.index)
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[(srrl_tmp.df['sky_status'] == 0) & (pred)]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[(srrl_tmp.df['sky_status'] == 1) & (~pred)]['GHI'], 'NSRDB clear only')
vis.add_circle_ser(test.df[(srrl_tmp.df['sky_status'] == 1) & (pred)]['GHI'], 'ML+NSRDB clear only')
# vis.add_line_ser(test.df['abs_ideal_ratio_diff'] * 100)
vis.show()
probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')
ground = cs_detection.ClearskyDetection.read_pickle('abq_ground_1.pkl.gz')
ground.df.index = ground.df.index.tz_convert('MST')
ground.trim_dates('10-01-2015', '10-08-2015')
ground.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
ground.scale_by_irrad('Clearsky GHI pvlib')
test = ground
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 61, by_day=True, multiproc=True)
pred = pred.astype(bool)
vis = visualize.Visualizer()
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 0) & (pred)]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 1) & (~pred)]['GHI'], 'PVLib clear only')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 1) & (pred)]['GHI'], 'ML+PVLib clear only')
# vis.add_line_ser(test.df['abs_ideal_ratio_diff'] * 100)
vis.show()
probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')
ground = cs_detection.ClearskyDetection.read_pickle('abq_ground_1.pkl.gz')
ground.df.index = ground.df.index.tz_convert('MST')
ground.trim_dates('10-01-2015', '10-08-2015')
ground.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
ground.scale_by_irrad('Clearsky GHI pvlib')
ground.df = ground.df.resample('30T').apply(lambda x: x[len(x) // 2])
test= ground
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 3, by_day=True, multiproc=True)
pred = pred.astype(bool)
vis = visualize.Visualizer()
abq_tmp = cs_detection.ClearskyDetection(nsrdb_abq.df)
abq_tmp.intersection(ground.df.index)
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[(abq_tmp.df['sky_status'] == 0) & (pred)]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[(abq_tmp.df['sky_status'] == 1) & (~pred)]['GHI'], 'NSRDB clear only')
vis.add_circle_ser(test.df[(abq_tmp.df['sky_status'] == 1) & (pred)]['GHI'], 'ML+NSRDB clear only')
# vis.add_line_ser(test.df['abs_ideal_ratio_diff'] * 100)
vis.show()
probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')
ground = cs_detection.ClearskyDetection.read_pickle('ornl_ground_1.pkl.gz')
ground.trim_dates('10-01-2008', '10-02-2008')
ground.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
ground.scale_by_irrad('Clearsky GHI pvlib')
ground.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status pvlib')
test = ground
# pred = clf.predict(test.df[feature_cols].values)
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 61, by_day=True, multiproc=True)
pred = pred.astype(bool)
vis = visualize.Visualizer()
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 0) & (pred)]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 1) & (~pred)]['GHI'], 'PVLib clear only')
vis.add_circle_ser(test.df[(test.df['sky_status pvlib'] == 1) & (pred)]['GHI'], 'ML+PVLib clear only')
# vis.add_line_ser(test.df['abs_ideal_ratio_diff'] * 100)
vis.show()
probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')
ground = cs_detection.ClearskyDetection.read_pickle('ornl_ground_1.pkl.gz')
ground.df.index = ground.df.index.tz_convert('EST')
ground.trim_dates('10-01-2008', '10-08-2008')
ground.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
ground.scale_by_irrad('Clearsky GHI pvlib')
ground.df = ground.df.resample('30T').apply(lambda x: x[len(x) // 2])
test= ground
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 3, by_day=True, multiproc=True)
pred = pred.astype(bool)
vis = visualize.Visualizer()
ornl_tmp = cs_detection.ClearskyDetection(nsrdb_ornl.df)
ornl_tmp.intersection(ground.df.index)
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[(ornl_tmp.df['sky_status'] == 0) & (pred)]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[(ornl_tmp.df['sky_status'] == 1) & (~pred)]['GHI'], 'NSRDB clear only')
vis.add_circle_ser(test.df[(ornl_tmp.df['sky_status'] == 1) & (pred)]['GHI'], 'ML+NSRDB clear only')
# vis.add_line_ser(test.df['abs_ideal_ratio_diff'] * 100)
vis.show()
probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas
visualize.plot_ts_slider_highligther(test.df, prob='probas')
vis = visualize.Visualizer()
vis.add_bar(feature_cols, clf.feature_importances_)
vis.show()
import pickle
with open('trained_model.pkl', 'wb') as f:
pickle.dump(clf, f)
with open('trained_model.pkl', 'rb') as f:
new_clf = pickle.load(f)
new_clf is clf
clf.get_params()
new_clf.get_params()